Interventions to improve patient-centered care for persons with multimorbidity are in constant growth. To date, the emphasis has been on two separate kinds of interventions, those based on a patient-centered care approach with persons with chronic disease and the other ones created specifically for persons with multimorbidity. Their effectiveness in primary healthcare is well documented. Currently, none of these interventions have synthesized a patient-centered care approach for care for multimorbidity. The objective of this project is to determine the particular elements of patient-centered interventions and interventions for persons with multimorbidity that are associated with positive health-related outcomes for patients. A scoping review was conducted as the method supports the rapid mapping of the key concepts underpinning a research area and the main sources and types of evidence available. A five-stage approach was adopted: (1) identifying the research question; (2) identifying relevant studies; (3) selecting studies; (4) charting the data; and (5) collating, summarizing and reporting results. We searched for interventions for persons with multimorbidity or patient-centered care in primary care. Relevant studies were identified in four systematic reviews (Smith et al. (2012;2016), De Bruin et al. (2012), and Dwamena et al. (2012)). Inductive analysis was performed. Four systematic reviews and 98 original studies were reviewed and analysed. Elements of interventions can be grouped into three main types and clustered into seven categories of interventions: 1) Supporting decision process and evidence-based practice; 2) Providing patient-centered approaches; 3) Supporting patient self-management; 4) Providing case/care management; 5) Enhancing interdisciplinary team approach; 6) Developing training for healthcare providers; and 7) Integrating information technology. Providing patient-oriented approaches, self-management support interventions and developing training for healthcare providers were the most frequent categories of interventions with the potential to result in positive impact for patients with chronic diseases. This scoping review provides evidence for the adaption of patient-centered interventions for patients with multimorbidity. Findings from this scoping review will inform the development of a toolkit to assist chronic disease prevention and management programs in reorienting patient care.

PURPOSE We wanted to assess the effectiveness of intensive education for physicians compared with a traditional session on communicating with breast cancer patients.

The Patient-Centered Clinical Method (PCCM) has been a core tenet of the practice and teaching of medicine since the first edition of Patient-Centered Medicine - Transforming the Clinical Method was published in 1995. This timely fourth edition continues to define the principles underpinning the patient-centered clinical method using four major components, clarifying its evolution and consequent development, and it brings the reader fully up to date. It reinforces the relevance of the method in the current much-changed realities of health care in a world where virtual care will remain common, dependence on technology is rising, and societal changes away from compassion, equity, and relationships toward confrontation, inequity, and self-absorption. Fully revised by its highly experienced author team ensuring wide interest and written for those practising now and for the practitioners of the future, this new edition will be welcomed by a wide international audience comprising all health professionals from medicine, nursing, social work, occupational therapy, physical therapy, pharmacy, veterinary medicine, and other fields. <!----> <!----> <!----> <!---->

The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs. Using an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products. A set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight). This paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.

Background: Social aspects play an important role in individual health and should be taken into consideration in the long-term care for people with multimorbidity. Purposes: To describe social vulnerability, to examine its correlation with the number of chronic conditions, and to investigate which chronic conditions were significantly associated with the most socially vulnerable state in patients with multimorbidity. Methods: Cross-sectional analysis from the baseline data of the Patient-Centred Innovations for Persons with Multimorbidity (PACEinMM) Study. Participants were patients attending primary healthcare settings in Quebec, Canada. A social vulnerability index was applied to identify social vulnerability level. The index value ranges from 0 to 1 (1 as the most vulnerable). Spearman’s rank correlation coefficient was calculated for the correlation between the social vulnerability index and the number of chronic conditions. Logistic regression was applied to investigate which chronic conditions were independently associated with the most socially vulnerable state. Results: There were 301 participants, mean age 61.0 ± 10.5, 53.2% female. The mean number of chronic health conditions was 5.01 ± 1.82, with the most common being hyperlipidemia (78.1%), hypertension (69.4%), and obesity (54.2%). The social vulnerability index had a median value of 0.13 (range 0.00–0.78). There was a positive correlation between the social vulnerability index and the number of chronic conditions (r = 0.24, p < 0.001). Obesity, depression/anxiety, and cardiovascular diseases were significantly associated with the most socially vulnerable patients with multimorbidity. Conclusions: There was a significant correlation between social vulnerability and the total number of chronic conditions, with depression/anxiety, obesity, and cardiovascular diseases being the most related to social vulnerability.